Why Your Swing Tracker Is Only Telling You Half the Story
By Ken Cherryhomes ©2025
Want to see why traditional swing trackers don’t tell the whole story? Watch this short video to understand the limitations of systems like Blast Motion and how xFactor Swing Dynamics Pro™ (formerly the xFactor Swing Time Calculator) delivers real, actionable data that actually helps hitters improve.
Introduction
If you’ve been using a Blast Motion sensor, you probably think you have a solid understanding of your hitters’ swings. After all, you’re getting bat speed, attack angles, and swing time—sounds like all the key pieces, right?
But let me ask you this:
- Does it tell you how long it actually takes a hitter to start their swing after deciding to swing?
- Does it show you how their swing time changes based on pitch location?
- Does it tell you if they’re becoming inconsistent over time, developing mechanical inefficiencies, or showing signs of increased reaction time or latency?
Because if it doesn’t, you’re only getting part of the picture.
The Problem With Traditional Swing Trackers
Blast Motion and similar systems track bat movement and provide post-swing metrics such as bat speed, attack angle, and swing path. They capture what happened in the swing but do not provide structured, time-based data that can be integrated into systems designed to improve decision-making, timing efficiency, or adaptability across different pitch locations.
xFactor Swing Dynamics Pro™ goes beyond standard motion tracking by providing objective, time-based swing efficiency metrics such as Swing Delay™, TTI, and Contact Points Mapping. These metrics allow for deeper analysis into how efficiently a hitter executes their swing, independent of swing philosophy or mechanical interpretation.
It isn’t just about how fast the bat moves. It’s about when the bat moves and how efficiently it gets to the ball. That’s where traditional swing trackers fail.
What xFactor Swing Dynamics Pro™ Does Differently
xFactor Swing Dynamics Pro™ captures key orientation metrics like vertical bat angle (VBA), horizontal bat angle (HBA), and attack angle (AA), just like Blast Motion. However, unlike Blast, xFactor goes beyond standard motion tracking by providing the missing half of the equation: actual swing efficiency.
Here’s what that means:
- Swing Delay™ – The Missing Metric in Swing Tracking Every hitter has a reaction time plus some level of hesitation or mechanical delay before their swing actually starts. This is time lost—and no other system measures it.
- Time to Impact (TTI) – A Complete Swing Efficiency Metric Most systems measure mechanical swing time (how long it takes to move the bat to contact). But that’s only part of the picture. TTI includes both Swing Delay™ and Mechanical Swing Time, so you know exactly how long it takes to go from decision to contact.
- Contact Points Mapping – Real Swing Timing, Not Just a Number A hitter’s swing time isn’t the same for every pitch. xFactor maps swing times for all 25 contact points in the zone so hitters and coaches know how their timing shifts based on pitch location.
- Swing Consistency Analysis – Are You Getting Better or Worse? Timing isn’t just about a single swing—it’s about how consistent a hitter is over time. If they’re slowing down, becoming inconsistent, or developing a hitch, xFactor tracks that so you can fix it before it becomes a problem.
- Tempo Trainer – Training That Actually Uses Real Swing Data Most training tools guess at timing. xFactor’s Tempo Trainer uses a hitter’s actual swing metrics to create real-time training cues that help build consistency.
Why Some Metrics Are Gone (And Why That’s a Good Thing)
If you’ve been using Blast Motion, you might notice that xFactor Swing Dynamics Pro™ doesn’t include bat speed (MPH) or hand speed metrics. That’s intentional.
Here’s why:
- Bat Speed Readings from IMU Sensors Are Inaccurate
Even the companies that sell them acknowledge that these numbers are extrapolated, not directly measured. Blast Motion has publicly stated that their bat speed measurements have an admitted margin of error of ±3 mph. Other IMU-based systems report similar variances.
At first glance, a ±3 mph error might seem small, but a ±3 mph error isn’t just a small difference—it means no reliable baseline for tracking progress. For example, if your sensor is consistently off by ±3 mph, this means the actual bat speed could vary by as much as 3 mph in either direction creating a potential 6 mph discrepancy in the captured speed, leading to inaccurate performance tracking. Without consistent baselines, it’s difficult to accurately measure improvements or identify inefficiencies over time.
This lack of precision leads to a poor understanding of swing progression and makes it harder to optimize training. These errors are compounded when considering attack angle and bat orientation variances, making bat speed unreliable as a meaningful metric for improvement.
The xFactor Swing Dynamics Pro™ focuses on time-based metrics because time is directly measurable, universally precise, and not subject to the compounding errors that plague bat speed calculations.
- 3D Swing Plane Visualizations Don’t Help You Hit They look cool but don’t provide actionable data. A visualized bat path doesn’t help a hitter eliminate inefficiencies or get more consistent, due to the variations in dynamics location to location. xFactor replaces flashy visuals with real, measurable data.
- No Arbitrary Post-Swing “Score” or Rating System Who decides what a “good” swing is, some algorithm trained on a subjective swing philosophy? A swing isn’t a video game. Scoring systems don’t actually help hitters improve—they don’t objectively identify inefficiencies, and they don’t track progress in a way that leads to real improvement. xFactor doesn’t assign arbitrary grades or impose subjective swing philosophies. We provide real, objective data that allows hitters to focus on what actually matters—refining their technique and building consistent performance, no matter their approach.
Beyond just choosing the right metrics, a swing tracking system should also be able to evolve with modern training technologies. The real test of value isn’t just what data it collects—it’s how well that data integrates into larger performance systems. That’s where scalability determines whether a system is just a data collector or a true training tool—an ‘x’ factor in its own right.
Scalability and Integration
Swing tracking data isn’t just about collecting numbers—it’s about how that data can be applied to improve performance. In order to keep up with advancements in training and technology, a system should be able to scale into AI-driven training, predictive modeling, and video analysis. A system designed for real performance optimization should do more than just collect numbers—it should provide insights that help hitters make better decisions and improve efficiency.
AI and Data-Driven Training
AI in sports training isn’t just about analyzing past performance—it’s about identifying inefficiencies and making real-time, adaptive training adjustments. For AI to be useful in swing analysis, it needs structured, interconnected data that provides more than just isolated swing metrics.
- Blast Motion collects static, post-swing data points (bat speed, attack angles, and swing time).
- AI can recognize patterns in these numbers, but it cannot generate real-time feedback, adjust training strategies dynamically, or track swing efficiency over time.
- Without structured data relationships, AI is limited to retrospective analysis rather than driving real-time improvements.
Swing Dynamics Pro is designed for real-time adaptability in AI-driven models.
- Provides spatial/temporal mapping of swing execution, so AI can analyze not just how fast a swing happens, but how efficiently it adapts to different pitch locations.
- Uses Swing Delay™, TTI, and Contact Points Mapping to allow AI-driven models to: Adjust training cues based on a hitter’s actual swing efficiency. Modify pitch sequencing for progressive challenge-based training. Detect developing inefficiencies, fatigue, or swing inconsistencies over time.
Progressive Modeling and Training Adaptation
For training to be effective, it should evolve as a player improves. Progressive modeling systems work best when they track performance over time, highlighting trends, inconsistencies, and areas for refinement.
- Blast Motion’s data captures only individual swing snapshots, making it difficult to integrate into progressive training models.
- There is no system in place to track whether a hitter’s efficiency is improving, regressing, or adapting to different pitch speeds and locations.
Swing Dynamics Pro is built for progressive adaptation.
- Tracks swing efficiency across multiple sessions, allowing training models to evolve based on a hitter’s actual performance changes.
- With real-time swing consistency analysis, hitters and coaches can identify trends in efficiency, fatigue, or developing hitches before they become major issues.
Video Analysis Integration
Video is a powerful tool for coaching, but its effectiveness depends on how well the data integrates with visual analysis.
- Blast Motion can sync with video, but it does not provide synchronized efficiency metrics that show where a hitter is gaining or losing time within the swing.
- Coaches must rely on subjective interpretation rather than objective, data-backed insights.
Swing Dynamics Pro seamlessly integrates with video by mapping efficiency metrics to specific points in the swing sequence.
- Rather than just reviewing swing path visually, hitters and coaches can see exactly where hesitation, inefficiencies, or inconsistencies occur.
- This allows for precise, data-driven adjustments rather than guessing based on visual patterns alone.
The Difference Between Data Collection and Performance Optimization
A good swing tracker collects data. A great one turns that data into solutions. But AI alone isn’t the solution—it’s what the AI is built on that matters.
Blast Motion now markets its AI-driven analytics as an advancement, but AI is only as good as the data feeding it. If that data is incomplete, subjective, or designed to reinforce a specific swing philosophy, then the AI isn’t helping hitters make better decisions—it’s just automating an existing bias.
xFactor Swing Dynamics Pro™ was designed differently. Its data isn’t locked into a single mechanical viewpoint, nor is it limited to post-swing analysis. Instead, it provides structured, objective metrics that allow AI and predictive systems to enhance decision-making, not dictate swing changes.
While Blast’s AI may attempt to suggest swing adjustments, xFactor’s system enables AI to refine timing, optimize plate approach, and improve training adaptability across multiple hitting philosophies. This is the difference between AI that reinforces a system’s bias and AI that empowers better decisions based on real, scalable data.
Why This Matters for Coaches and Players
Blast Motion is a data collection tool—it gives you bat speed, swing path, and attack angles. That’s useful, but it doesn’t tell you why a hitter is early or late, or how efficient their swing actually is.
xFactor Swing Dynamics Pro™ is a solution—it tells you when, why, and how efficiently a hitter executes their swing.
If you’re serious about understanding swing timing and eliminating inefficiencies, it’s time to move beyond just tracking swings—it’s time to fix them.